Image processing determines the location of a particular anatomical feature or body part from the medical image. Machine learning may be used to detect a landmark in a medical image. Machine learning involves training to distinguish the appearance of a landmark from the appearance of rest of the medical image. Relying on appearance may yield false positives, such as from variations in scanned image data, unusual patient pathologies, motion artifacts, image artifacts from metal implants, low doses during image acquisition and other noise. If the landmark is not in the field of view of the scanner, the trained classifier may identify an incorrect feature as the landmark (i.e., false positives).
One solution for minimizing false positives is to detect multiple landmarks and to use the spatial or geometric relationship between the landmarks to rule out false positives, such as with a voting scheme. Non-maximal suppression may be used, where “modes” of a distribution are selected as candidate locations. The combination of possible landmark configurations grows exponentially as the number of landmarks and candidate locations increases, requiring specialized models and/or algorithms. Markov Random Field (MRF) or Conditional Random Field (CRF) are specialized and may result in accurate landmark detection, but the analysis is complicated and computationally intensive, thus approximation techniques are often used when the landmark configuration and underlying graph has loops. Another example of a specialized model uses heuristic voting in which a small set of candidate locations vote on each other. MRF and CRF models and heuristic voting all suffer from false negatives. When a landmark is outside of the field of view, the aforementioned models may assign a “virtual” candidate location denoting the absence of a landmark, which may be selected in a false positive. Designing for virtual candidates is complex and may require assigning a probability on how likely a landmark is absent, which is empirical and may not be accurate.